Short and comprehensive manual to extend your local matlab with a high performance computing cluster of NVidia tesla's 2070 graphical processing units.
I am Susan C. I am an Instant DBMS Homework Expert at databasehomeworkhelp.com. I hold a Master’s Degree in Programming, from Leeds, UK. I have been helping students with their homework for the past 9 years. I solve homework related to Instant DBMS.
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I am Susan C. I am an Instant DBMS Homework Expert at databasehomeworkhelp.com. I hold a Master’s Degree in Programming, from Leeds, UK. I have been helping students with their homework for the past 9 years. I solve homework related to Instant DBMS.
Visit databasehomeworkhelp.com or email info@databasehomeworkhelp.com. You can also call on +1 678 648 4277 for any assistance with Instant DBMS Homework.
INTRODUCTION TO MACHINE LEARNING FOR MATERIALS SCIENCEIPutuAdiPratama
This lab introduces students to an end-to-end example of applying a machine learning (ML) workflow to a materials science dataset to address a research problem. The lab aims at deepening the conceptual understanding of ML, and while procedural skills such as writing Python code are not the focus of this lab, students will gain experience with a number of standard open source packages by interacting with code snippets through the Jupyter Notebook format and describing what each essential command does.
Slowly the power of mysqlnd plugins become visible. Mysqlnd plugins challenge MySQL Proxy and are often a noteworthy, if not superior, alternative alternative to MySQL Proxy for PHP users. Plugins can do almost anything that MySQL Proxy can do - but on the client. Please find details in the slides. The presentation has been given today at the PHP track on FrOSCon.
Strata CA 2019: From Jupyter to Production Manu MukerjiManu Mukerji
Proposed title
From Jupyter to production
Description of the presentation
Jupyter is very popular for data science, data exploration and visualization, this talk is about how to use it in for AI/ML in a production environment.
General Flow of talk:
How things can go wrong with QA, Production releases when using a notebook
Common Jupyter ML examples
Standard ML flow
Training in production
Model creation
Testing in production
Papermill and Jupyter
Production workflows with Sagemaker
Speaker
Manu Mukerji is senior director of data, machine learning, and analytics at 8×8. Manu’s background lies in cloud computing and big data, working on systems handling billions of transactions per day in real time. He enjoys building and architecting scalable, highly available data solutions and has extensive experience working in online advertising and social media.
SMP4: Thread Scheduler
======================
INSTRUCTIONS
============
1. OVERVIEW
===========
In this MP, you will write a user-mode thread scheduler. The basic purpose
of a scheduler is to multiplex use of the computer across several threads
of execution. This MP deals with two different scheduling policies: FIFO
and Round Robin. You will implement both, for use in a simple cooperative
multi-threading system. Along the way, you'll also learn about implementing
object-oriented constructs in low-level procedural languages like C.
This assignment consists of implementing the core functionality of the
scheduler (Step 4) and answering 10 questions (Step 5). Code for
Step 4 goes in sched_impl.c and sched_impl.h.
2. THEORY OF OPERATION
======================
The given code in the MP defines the skeleton of a scheduler together with a
parameterized dummy workload. The idea is when you run the MP, you specify
a scheduling policy, scheduler queue size, some number of worker threads to
create, and, optionally, the number of iterations for which the worker
threads should run. The basic code that parses command line arguments and
creates these worker threads is provided in the MP, but you must implement
the core synchronization and scheduling operations.
As provided, the MP only includes the "dummy" scheduling algorithm, which
doesn't even try to do anything. You can run it like this:
make
./scheduler -dummy 0 N # where N is some number of worker threads
All threads run right away regardless of the queue size (even zero!), and
are scheduled by the operating system. The goal of this MP is to create
scheduler implementations which are a bit more controlled and predictable.
For example, once you have completed the MP, the following should work:
./scheduler -fifo 1 2 3
Main: running 2 workers on 1 queue_size for 3 iterations
Main: detaching worker thread 3075984304
Main: detaching worker thread 3065494448
Main: waiting for scheduler 3086474160
Thread 3075984304: in scheduler queue
Thread 3075984304: loop 0
Thread 3075984304: loop 1
Thread 3075984304: loop 2
Thread 3075984304: exiting
Thread 3065494448: in scheduler queue
Thread 3065494448: loop 0
Thread 3065494448: loop 1
Thread 3065494448: loop 2
Thread 3065494448: exiting
Scheduler: done!
The command line options used above specify:
-fifo Use FIFO scheduling policy
1 One thread can be in the scheduler queue at a time
2 Create 2 worker threads
3 Each thread runs for 3 time slices
Here's another example:
./scheduler -rr 10 2 3
Main: running 2 workers on 10 queue_size for 3 iterations
Main: detaching worker thread 3075828656
Main: detaching worker thread 3065338800
Main: waiting for scheduler 3086318512
Thread 3075828656: in scheduler queue
Thread 3065338800: in scheduler queue
Thread 3075828656: loop 0
Thread 3065338800: loop 0
Thread 3075828656: loop 1
T.
You are to simulate a dispatcher using a priority queue system.New.pdfgardenvarelianand
You are to conduct a sample survey to determine the mean family income in a rural area of
central Florida. The question is, how many families should be sampled? In a pilot smaple of 10
families, the standard deviation of the sample was $500. The sponsor of the survey wants you to
use the 95 percent confidence level. the estimate is to be within $100. How many families should
be interviewed?
Solution
Given a=0.05, Z(0.025)=1.96 (from standard normal table)
So n=(Z*s/E)^2
=(1.96*500/100)^2
=96.04
Take n=97.
SMP4: Thread Scheduler (PART 1)
======================
INSTRUCTIONS
============
1. OVERVIEW
===========
In this MP, you will write a user-mode thread scheduler. The basic purpose
of a scheduler is to multiplex use of the computer across several threads
of execution. This MP deals with two different scheduling policies: FIFO
and Round Robin. You will implement both, for use in a simple cooperative
multi-threading system. Along the way, you'll also learn about implementing
object-oriented constructs in low-level procedural languages like C.
This assignment consists of implementing the core functionality of the
scheduler (Step 4) and answering 10 questions (Step 5). Code for
Step 4 goes in sched_impl.c and sched_impl.h.
2. THEORY OF OPERATION
======================
The given code in the MP defines the skeleton of a scheduler together with a
parameterized dummy workload. The idea is when you run the MP, you specify
a scheduling policy, scheduler queue size, some number of worker threads to
create, and, optionally, the number of iterations for which the worker
threads should run. The basic code that parses command line arguments and
creates these worker threads is provided in the MP, but you must implement
the core synchronization and scheduling operations.
As provided, the MP only includes the "dummy" scheduling algorithm, which
doesn't even try to do anything. You can run it like this:
make
./scheduler -dummy 0 N # where N is some number of worker threads
All threads run right away regardless of the queue size (even zero!), and
are scheduled by the operating system. The goal of this MP is to create
scheduler implementations which are a bit more controlled and predictable.
For example, once you have completed the MP, the following should work:
./scheduler -fifo 1 2 3
Main: running 2 workers on 1 queue_size for 3 iterations
Main: detaching worker thread 3075984304
Main: detaching worker thread 3065494448
Main: waiting for scheduler 3086474160
Thread 3075984304: in scheduler queue
Thread 3075984304: loop 0
Thread 3075984304: loop 1
Thread 3075984304: loop 2
Thread 3075984304: exiting
Thread 3065494448: in scheduler queue
Thread 3065494448: loop 0
Thread 3065494448: loop 1
Thread 3065494448: loop 2
Thread 3065494448: exiting
Scheduler: done!
The command line options used above specify:
-fifo Use FIFO scheduling policy
1 One thread can be in the scheduler queue at a time
2 Create 2 worker threads
3 Each thread runs for 3 time slices
Here's another example:
./scheduler -rr 10 2 3
Main: running 2 workers on 10 queue_size for 3 iterations
Main: detaching worker thread 3075828656
Main: detaching worker thread 3065338800
Main: waiting for scheduler 3086318512
Thread 3075828656: in scheduler queue
Thread 3065338800: in scheduler queue
Thread 3075828656: loop 0
Thread 3065338800: loop 0
Thread 3075828656: lo.
Biophysics by the sea 2016 program and abstract bookDirk Hähnel
Biophysics by the sea 2016 program and abstract book
International conference on fluorescence super-resolution microscopy, spectroscopy, molecular cell mechanics and theoretical neurophysics
26th. -30th. september 2016
Pollentia resort, Alcudia, Spain
Event organizer:
Georg August University
Third Institute of Physics
Dirk Hähnel
37077 Göttingen
FSM 2015 - International Biophysics ConferenceDirk Hähnel
Fluorescence Spectroscopy, Microscopy and Molecular Cell
Mechanics
Program and Abstract Book
8th – 11th October 2015
Pollentia Club Resort, 07400 Alcúdia, Spain
Event organizer:
Georg August University
Third Institute of Physics
Dirk Hähnel
Friedrich Hund Platz 1
37077 Göttingen, Germany
21st International Workshop on“Single Molecule Spectroscopy and Super-resolu...Dirk Hähnel
Filling the usability gap: Bioinformatics solutions for Image-Scanning Microscopy, Stochastic Optical Fluctuation Imaging, and Surface Single Molecule Experiments
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 4
HPC and HPGPU Cluster Tutorial
1. Cluster Tutorial
I. Introduction and prerequisits
This short manual should give guidance how to set up a proper workenvironment with the three different clusters which we have available in our institute. The first question to ask yourself is the following:”Do I need the cluster for my problem?”. From experience I can tell mostly not, because sometimes the cost to reprogram the solution in a parallel manner is excceding the benefit by far. Therefor please check the following questions, if you answer them with yes, it makes absolutly sense to solve your problem with the cluster.
I have a huge a set of data which won’t fit to the memory of a single machine?
I have a huge a set of data which won’t fit to the memory of a single machine, which I cannot split in different chunks because I have to jump back and fort within them?
I have plenty of iterations to perform for my simulation which I want to put on the cluster because it would take a couple of months otherwise.
The routine I wrote for the cluster can be used by my peers for the next ten years and will be used daily.
It might sound odd for some, but in general one should not underestimate the initial effort to get started. If you are still not deterred, you must make sure that the following conditions are met.
The special Matlab is installed on your office computer.
HPC Cluster Manager and Job Manager are installed on the very same machine.
You have an Active Directory account, aka PHYSIK3HansWurscht.
If those requirements are not met, please write a ticket to https://opto1:9676/portal describing that you want to participate in the cluster clique and we will set you up within the same moment.
We have three different clusters available, termed ALPHA, GAMMA and SKYNET. They do all serve different purposes, thus it makes total sense to fit your problem to the specific grid.
SKYNET: Is a HPGPU (High performance graphiccard processing unit) which is very experimentally and needs a high degree of expertise. But you can also run regular jobs here, it is not forbidden. It has up to 80 Workers. It has eight M2050 Tesla GPU’s, which are pretty insane.
ALPHA: Is a HPC which makes use of the office computers when those are non busy, for example at night or on weekends. Since that cluster can shrink and grow depending on available resources there is no absolute number available, but the maximum is somewhere around 500 workers.
GAMMA: Is a HPC with 16 Workers but 32GB Memory, in case one must submitt a job with a huge requirement in terms of memory it is recomendet to use this grid.
2. II. Connect to the cluster
Connecting to the cluster is easy as making coffee. Please download the profile from the project server https://projects.gwdg.de/projects/cluster and import them to your local Matlab application Fig.2. Afterwards it is recommended to run the test routines which are checking your configuration. It is very important that they are all marked as passed Fig.4. Where to find the button is shown in Fig.1. It might be that the system is asking for authentication ones, therefore please connect with your regular office computers credentials, the dialog window which appears looks like in Fig.3
Abbildung 1: Manage Cluster
Abbildung 2: Import or find Clusters
Abbildung 3: Connect to cluster with AD credentials
Press here and click manage Clusters
3. Abbildung 4: Test Cluster Connectivity
III. Monitor the jobs
On the local computer one has a program called job manager, which is used to monitor the cluster resources. If for instance a job hangs up or one wants to chancel, this program is the necessary tool.
In Fig.5 the typical layout of the job manager is displayed, to chancel you job, right click on it and chancel. To control different clusters, one needs to set the job manager to right cluster headnode, which is shown in Fig. 6. It is very important that you kill your jobs if they hang up, otherwise the other users of the cluster cannot use it at full resource level.
Abbildung 5:Job Manager
Abbildung 6: Select Head Node
4. IV. Programming Tutorial
The programs which are explained in this tutorial are available for direct use in Matlab Editor, please visit the project server: https://projects.gwdg.de/projects/cluster and download folder example files. Add them to your local Matlab path otherwise the interpreter cannot find them. First you will need to select the parallel configuration; this can be a cluster or your local machine if it consists of several CPU cores. Fig.7.
Abbildung 7:Select Profile
For more information on configurations and programming with user configurations, see:
http://www.mathworks.com/access/helpdesk/help/toolbox/distcomp/index.html?/access/helpdesk/help/toolbox/distcomp/f5-16141.html#f5-16540
5. A. Using an Interactive MATLAB pool
To interactively run your parallel code, you first need to open a MATLAB pool. This reserves a collection of MATLAB worker sessions to run your code. The MATLAB pool can consist of MATLAB sessions running on your local machine or on a remote cluster. In this case, we are initially running on your local machine. You can use matlabpool open to start an interactive worker pool. If the number of workers is not defined, the default number defined in your configuration will be used. A good rule of thumb is to not open more workers then cores available. If the Configuration argument is not provided, matlabpool will use the default configuration as setup in the beginning of this section. When you are finished running with your MATLAB pool, you can close it using matlabpool close. Two of the main parallel constructs that can be run on a MATLAB pool are parfor loops (parallel for-loops) and spmd blocks (single program - multiple data blocks). Both constructs allow for a straight- forward mixture of serial and parallel code.
parfor loops are used for task-parallel (i.e. embarrassingly parallel) applications. parfor is used to speed up your code. Below is a simple for loop converted into a parfor to run in parallel, with different iterations of the loop running on different workers. The code outside the parfor loop executes as traditional MATLAB code (serially, in your client MATLAB session).
Different workers. The code outside the parfor loop executes as traditional MATLAB code (serially, in your client MATLAB session).
Note: The example below is located in the m-file, ‘parforExample1.m’.
matlabpool open 2 % can adjust according to your resources
N = 100;
M = 200;
a = zeros(N,1);
tic; % serial (regular) for-loop for i = 1:N
a(i) = a(i) + max(eig(rand(M)));
end toc;
tic; % parallel for-loop parfor i = 1:N
a(i) = a(i) + max(eig(rand(M)));
end toc;
matlabpool close
spmd blocks are a single program multiple data (SPMD) language construct. The "single program" aspect of spmd means that the identical code runs on multiple labs. The code within the spmd body executes simultaneously on the MATLAB workers. The "multiple data" aspect means that even though the spmd statement runs identical code on all workers, each worker can have different, unique data for that code. spmd blocks are useful when dealing with large data that cannot fit on a single machine. Unlike parfor, spmd blocks support inter-worker communication. They allow:
Arrays (and operations on them) to be distributed across multiple workers
Messages to be explicitly passed amongst workers.
6. The example below creates a distributed array (different parts of the array are located on different workers) and computes the svd of this distributed array. The spmd block returns the data in the form of a composite object (behaves similarly to cells in serial MATLAB. For specifics, see the documentation link below).
Note: The example below is located in the m-file, ‘spmdExample1.m’.
matlabpool open 2 % can adjust according to your resources
M = 200;
spmd
N = rand(M,M,codistributor); % 200x100 chunk per worker
A = svd(N);
end
A = max(A{1}); % Indexing into the composite object
disp(A)
clear N
matlabpool close
For information on matlabpool, see:
http://www.mathworks.com/access/helpdesk/help/toolbox/distcomp/index.html?/access/helpdesk/help/toolbox/distcomp/ matlabpool.html
For information about getting started using parfor loops, see:
http://www.mathworks.com/access/helpdesk/help/toolbox/distcomp/index.html?/access/helpdesk/help/toolbox/distcomp/ brb2x2l-1.html
For information about getting started using spmd blocks, see: http://www.mathworks.com/access/helpdesk/help/toolbox/distcomp/index.html?/access/helpdesk/help/toolbox/distcomp/ brukbno-2.html
For information regarding composite objects:
http://www.mathworks.com/access/helpdesk/help/toolbox/distcomp/index.html?/access/helpdesk/help/toolbox/distcomp/ brukctb-1.html
For information regarding distributed arrays:
http://www.mathworks.com/access/helpdesk/help/toolbox/distcomp/index.html?/access/helpdesk/help/toolbox/distcomp/bqi9fln-1.html
7. 1. Using Batch to Submit Serial Code – Best Practice for Scripts
batch sends your serial script to run on one worker in your cluster. All of the variables in
your client workspace (e.g. the MATLAB process you are submitting from) are sent to the
worker by default. You can alternatively
pass a subset of these variables by
defining the Workspace argument and
passing the desired variables in a
structure. After your job has finished, you
can use the load command to retrieve
the results from the worker-workspace
back into your client-workspace. In this and all examples following, we use a wait to ensure
the job is done before we load back in worker-workspace. This is optional, but you can not
load the data from a task or job until that task or job is finished. So, we use wait to block the
MATLAB command line until that occurs. If the Configuration argument is not provided,
batch will use the default configuration that was set up above.
Note: For this example to work, you will need ‘testBatch.m’ on the machine that you are
submitting from (i.e. the client machine). This example below is located in the m-file,
‘submitJob2a.m’.
%% This script submits a serial script using batch
job2a = batch('testBatch');
wait(job2a); % only can load when job is finished
sprintf('Finished Running Job')
load(job2a); % loads all variables back
sprintf('Loaded Variables into Workspace')
% load(job2a, 'A'); % only loads variable A
destroy(job2a) % permanently removes job data
sprintf('Test Completed')
If you have submitted successfully, you should see the following variables appear in your
client workspace:
Abbildung 9: Workspace
For more information on batch, see:
http://www.mathworks.com/access/helpdesk/help/toolbox/distcomp/index.html?/access/helpdesk/help/toolbox/distc
omp/batch.html
and here:
Abbildung 8: Batch Job
8. http://www.mathworks.com/access/helpdesk/help/toolbox/distcomp/index.html?/access/helpdesk/help/toolbox/distcomp/brjw1e5-1.html#brjw1fx-3
2. Using Batch to Submit Scripts that Run Using a MATLAB pool
batch with the 'matlabpool' option sends scripts containing parfor or spmd to run on workers via a MATLAB pool. In this process, one worker behaves like a MATLAB client process that facilitates the distribution of the job amongst the workers in the pool and runs the serial portion of the script. Therefore, specifying a 'matlabpool' of size N actually will result in N+1 workers being used. Just like in step 2a, all variables are automatically sent from your client workspace (i.e. the workspace of the MATLAB you are submitting from) to the worker’s workspace on the cluster. load then brings the results from your worker’s workspace back into your client’s workspace. If a configuration is not specified, batch uses the default configuration as defined in the beginning of this section.
Note: For this example to work, you will need ‘testParforBatch.m’ on the machine that you are submitting from (i.e. the client machine). This example below is located in the m-file, submitJob2b.m.
%% This script submits a parfor script using batch
job2b = batch('testParforBatch','matlabpool',2);
wait(job2b); % only can load when job is finished sprintf('Finished Running Job')
load(job2b); % loads all variables back
sprintf('Loaded Variables into Workspace')
% load(job2b, 'A'); % only loads variable A
destroy(job2b) % permanently removes job data
sprintf('Test Completed')
If you have submitted successfully, you should see the following variables appear in your client workspace:
Abbildung 10: Workspace Batch Pool
9. The above code submitted a script containing a parfor. You can submit a script containing a
spmd block in the same fashion by changing the name of the submission script in the batch
command. Note: For this example to work, you will need ‘testSpmdBatch.m’ on the machine
that you are submitting from (i.e. the client machine). This example below is located in the m-file,
submitJob2b_spmd.m.
%% This script submits a spmd script using batch
job2b = batch('testSpmdBatch','matlabpool',2);
wait(job2b); % only can load when job is finished
sprintf('Finished Running Job')
load(job2b); % loads all variables back
sprintf('Loaded Variables into Workspace')
% load(job2b, 'A'); % only loads variable A
destroy(job2b) % permanently removes job data
sprintf('Test Completed')
If you have submitted successfully, you should see the following variables appear in your
client workspace:
Abbildung 11: Batch Pool SPMD
10. B. Run Task-Parallel Example with Jobs and Tasks
In this example, we are sending a task parallel job with multiple tasks. Each task evaluates the built-in MATLAB function. The createTask function in the below example is passed the job, the function to be run in the form of a function handle (@sum), the number of output arguments of the function (1), and the input argument to the sum function in the form of a cell array ({[1 1]});
If not given a configuration, findResource uses the scheduler found in the default configuration defined in the beginning of this section.
Note: This example is located in the m-file, ‘submitJob3a.m’.
%% This script submits a job with 3 tasks
sched = findResource();
job3a = createJob(sched);
createTask(job3a, @sum, 1, {[1 1]});
createTask(job3a, @sum, 1, {[2 2]}); createTask(job3a, @sum, 1, {[3 3]}); submit(job3a)
waitForState(job3a, 'finished') %optional
sprintf('Finished Running Job')
results = getAllOutputArguments(job3a);
sprintf('Got Output Arguments')
destroy(job3a) % permanently removes job data
sprintf('Test Completed')
If you have submitted successfully, you should see the following variables appear in your client workspace:
Abbildung 12: Parallel Task
results should contain the following:
Abbildung 13: Terminal Output Task Parallel
11. You can also call a user-created function in the same way as shown above. In that case, you will need to make sure that any scripts, files, or functions that the task function uses are accessible to the cluster. You can do this by sending those files to the cluster via the FileDependencies property or by directing the worker to a shared directory containing those files via the PathDependencies property. An example of using FileDependencies is shown below: Note: you will need to have a ‘testTask.m’ file on the machine you are submitting from for this example to work. This example is located in the m- file, ‘submitJob3b.m’.
% This script submits a job with 3 tasks
sched = findResource();
job3b = createJob(sched,'FileDependencies',{'testTask.m'});
createTask(job3b, @testTask, 1, {1,1});
createTask(job3b, @testTask, 1, {2,2}); createTask(job3b, @testTask, 1, {3,3}); submit(job3b)
waitForState(job3b, 'finished') % optional sprintf('Finished Running Job')
results = getAllOutputArguments(job3b);
sprintf('Got Output Arguments')
destroy(job3b) % permanently removes job data
sprintf('Test Completed')
If you have submitted successfully, you should see the following variables appear in your client workspace:
Abbildung 14: Task Parallel Workspace
12. results should contain the following:
Abbildung 15: Task Parallel Output
For more information on File and Path Dependencies, see the below documentation.
File Dependencies: http://www.mathworks.com/access/helpdesk/help/toolbox/distcomp/index.html?/access/helpdesk/help/toolbox/distcomp/filedependencies.html
Path Dependencies:
http://www.mathworks.com/access/helpdesk/help/toolbox/distcomp/index.html?/access/helpdesk/help/toolbox/distcomp/ pathdependencies.html
More general overview about sharing code between client and workers:
http://www.mathworks.com/access/helpdesk/help/toolbox/distcomp/index.html?/access/helpdesk/help/toolbox/distcomp/bqur7ev-2.html#bqur7ev-9
13. C. Run Task-Parallel Example with a MATLAB pool job - Best
Practice for parfor or spmd in functions
In this example, we are sending a MATLAB pool job with a single task. This is nearly equivalent
to sending a batch job (see step 2b) with a parfor or a spmd block, except this method is
best used when sending functions and not scripts. It behaves just like jobs/tasks
explained in step 3. The function referenced in the task contains a parfor.
Note: For this example to work, you will need ‘testParforJob.m’ on the machine that you are
submitting from (i.e. the client machine). This example is located in the m-file,
‘submitJob4.m’.
% This script submits a function that contains parfor
sched = findResource();
job4 = createMatlabPoolJob(sched,'FileDependencies',...
{'testParforJob.m'});
createTask(job4, @testParforJob, 1, {});
set(job4, 'MaximumNumberOfWorkers', 3);
set(job4, 'MinimumNumberOfWorkers', 3);
submit(job4)
waitForState(job4, 'finished') % optional
sprintf('Finished Running Job')
results = getAllOutputArguments(job4);
sprintf('Got Output Arguments')
destroy(job4) % permanently removes job data
sprintf('Test Completed')
If you have submitted successfully, you should see the following variables appear in your
client workspace:
results{1} should contain a [50x1 double].
For more information on creating and submitting MATLAB pool jobs, see
http://www.mathworks.com/access/helpdesk/help/toolbox/distcomp/index.html?/access/helpdesk/help/toolbox/distc
omp/creatematlabpooljob.html
Abbildung 16: WOrkspace Variables SPMD in Functions
14. D. Run Data-Parallel Example
In this step, we are sending a data parallel job with a single task. The format is similar to that of jobs/tasks (see step 3). For parallel jobs, you only have one task. That task refers to a function that uses distributed arrays, labindex, or some mpi functionality. In this case, we are running a simple built in function (labindex) which takes no inputs and returns a single output. labindex returns the ID value for each of worker processes that ran the it . The value of labindex spans from 1 to n, where n is the number of labs running the current job Note: This example is located in the m-file, ‘submitJob5.m’.
%% Script submits a data parallel job, with one task
sched = findResource();
job5 = createParallelJob(sched);
createTask(job5, @labindex, 1, {});
set(job5, 'MaximumNumberOfWorkers', 3);
set(job5, 'MinimumNumberOfWorkers', 3);
submit(job5)
waitForState(job5, 'finished') % optional
sprintf('Finished Running Job')
results = getAllOutputArguments(job5);
sprintf('Got Output Arguments')
destroy(job5); % permanently removes job data
sprintf('Test Completed')
If you have submitted successfully, you should see the following variables appear in your client workspace:
Abbildung 17: Workspace Data Parallel
15. results should contain the following:
Abbildung 18: Results Data Parallel
For more information on creating and submitting data parallel jobs, see:
http://www.mathworks.com/access/helpdesk/help/toolbox/distcomp/index.html?/access/helpdesk/help/toolbox/distcomp/createparalleljob.html
For more information on, labindex, see:
http://www.mathworks.com/access/helpdesk/help/toolbox/distcomp/index.html?/access/helpdesk/help/toolbox/distcomp/labindex.html
E. Node GPU Processing
If one needs to accelerate the execution even further a good strategy is to use the GPU CUDA building functions of Matlab distributed toolbox. Set the cluster profile to SKYNET and open a pool of workers.
Matlabpool open …
The Scheduler of located on the headnode of the cluster will recognize that your job has GPU code on board and will switch the scheduling profile automatically to dispatch only to the nodes which have a GPU integrated. In any case it is a good idea to catch the error if the scheduler will not work properly. See the next block of source code to see an excellent example how to do that.
16. function testGPUInParfor()
spmd
selectGPUDeviceForLab();
end
parfor i = 1:1000
% Each iteration will generate some data A
A = rand(5555);
if selectGPUDeviceForLab()
A = gpuArray(A);
disp( 'Do it on the GPU' )
else
disp( 'Do it on the host' )
end
% replace the following line with whatever task you need to do
S = sum(A,1);
% Maybe collect back from GPU (gather is a no-op if not on the GPU)
S = gather(S);
end
function ok = selectGPUDeviceForLab()
persistent hasGPU;
if isempty( hasGPU )
devIdx = mod(labindex-1,gpuDeviceCount())+1;
try
dev = gpuDevice( devIdx );
hasGPU = dev.DeviceSupported;
catch %#ok
hasGPU = false;
end
end
ok = hasGPU;
F. Avoid Errors – Use the Compiler To Your Advantage
It is counterintuitive that in a parallel loop or parallel data block, one single iteration of that particular block is running on a parallel task, aka a worker. Therefor the iterations must be iterations save, since there is no guaranty that the iterations are running in an ascending order. What I prefer to do is borrow the map reduce approach, of course we don’t reduce anything here but the design pattern is great to prevent you some headache. In the reduce step, a piece of data and function is scheduled to a worker, there the reduce function does produce a return. (In real map reduce one can now proced and use the output of step(x-1) in step(x), which we normally don’t.) I can only advice, make extensively use of function returns. The idea is that if a chunk of data is distributed via a dimension or a chunk of iterations are distributed over the sum of iterations that every slice goes via a function call to the worker and comes back via return to the parallel block. Besides it has advantages for the underlying MPI which would shovel all variables via multicast to all workers instead only to one worker, but this is above the scope of this document.
17. In addition it is very clever to write your function in a way that it can run on both, local and cluster environment. For reference see the example folder “sofi” on the project server.
Problem:
Parfor
u=u*v;
end
Better:
Parfor
u=multi(u,v);
end
G. Summary Chart for Scheduling Options
Abbildung 19: Scheduler Options
18. V. Glossary:
HPGPU: High performance graphiccard processing unit
Worker: A worker is a parallel task. In other words, if one as 100 workers available, one can run 100 iterations of a parallel loop in one time intervall (tick) of the processor.
Node: A node is a physical machine, for example a computer connected to a cluster is a node of that very cluster. This node can have 16 Workers, if the node has four CPU’s with for cores on each CPU.
MPI: Message Passing Interface is a fancy piece of software which distributes processes around in a grid via RPC’s.
RPC: Remote Proceture Calls